Smartwatch Occlusion Image Dataset
#Object Detection #Image Classification #Gesture Recognition #User Interaction Analysis #Product Testing
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-10-14
AI Analysis & Value Prop
The retail e-commerce industry is facing challenges with accurate gesture recognition and user interaction analysis due to obstructions such as hand gestures, wristbands, reflections, or blurred watch faces. Current solutions often lack robustness in varying lighting conditions and occlusion types, leading to poor user experience. This dataset aims to address these specific technical issues by providing a comprehensive collection of images highlighting various occlusion scenarios. The data was collected using high-resolution cameras in controlled environments, ensuring clarity and consistency. Quality control measures include multi-round annotations, consistency checks, and expert reviews to verify accuracy. The dataset is stored in JPG format, organized by occlusion type and timestamp for easy access and analysis.
Dataset Insights
Sample Examples
ce278d4a**.jpg|1224*1632|256.82 KB
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| image_brightness | float | The average brightness value of the image, used to evaluate the lighting conditions. |
| image_contrast | float | The contrast value of the image, reflecting the visual clarity of the image. |
| occlusion_level | string | The extent to which the smartwatch is obscured in the image, such as unobstructed, partially obstructed, or completely obstructed. |
| hand_gesture | string | Gesture recognition tags in the image, identifying the posture and movement of the hand. |
| watch_visibility | float | The proportion of the visible area of the smartwatch in the image, used to assess the watch's visibility. |
| lighting_condition | string | The lighting environment at the time the image was taken, such as daytime, nighttime, indoor, or outdoor. |
| image_blurriness | float | The degree of blur in the image, used to assess the clarity of the image. |
| background_clutter | string | The complexity of the image background, such as simple, ordinary, or complex. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What is the main purpose of the Smartwatch Occlusion Recognition Image Dataset?
- The dataset is primarily used for researching and developing recognition capabilities of smartwatches in occlusion scenarios, particularly in gesture recognition technology.
- What are the industry applications of the Smartwatch Occlusion Recognition Image Dataset?
- This dataset is widely used in the retail industry to enhance the target detection and occlusion handling capabilities of smartwatches in complex environments.
- How can the Smartwatch Occlusion Recognition Image Dataset be used for object detection?
- By training computer vision models with this dataset, the object detection capability of smartwatches in dynamic and occluded scenarios can be improved.
- How does the Smartwatch Occlusion Recognition Image Dataset support gesture recognition technology?
- By providing a rich set of images featuring gestures and occlusions, the dataset helps train models to improve the accuracy and robustness of gesture recognition.
- What types of occlusions are present in the Smartwatch Occlusion Recognition Image Dataset?
- The dataset includes various types of occlusions such as palms, clothing, and other accessories, which can be used to study the impact of occlusion on detection.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Smartwatch Occlusion Image Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/88815f105f0db1200556d98993645126},
urldate={2025-08-28},
keywords={smartwatch image dataset,gesture recognition,occlusion analysis,e-commerce data,user interaction dataset},
version={1.0}
}Using this in research? Please cite us.
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